These dialects, in turn, differ quite a bit from each other. But the work on dialects of Bangladesh is infrequent to our research. A lot of research has been performed to detect speeches, dialects and languages of different region throughout the world. We observed 6% improvement in ASR accuracy with phonetic system.Īutomatic recognition systems are generally applied successfully in speech processing to categorize observed utterances by the speaker identity, dialect and linguistic communication. This result is compared with implemented phonetic system which shows that ASR accuracy, using phonetic system is better than GMM. It is seen that GMM is better at the classification of signal data, outcomes of performance evaluation shows that GMM outperforms the other three classifiers in terms of accuracy by more than 20%. Different classification techniques are implemented and comparing accuracy of speech recognition of different classifier. Classifier is used to classify the fragmented phonemes or words after the fragmentation of the speech signal.
#MOBILE SPEECH TO TEXT SOFTWARE ANDROID#
The accuracy of speech recognition of ASR classifier and phonetic system is evaluated on day to day human to machine communications, using high-quality recording equipment, while the results for enhancement of existing systems is done on everyday android phones, and evaluated for normal conversations in Hindi and English language. matching phonemes and hence generates more correct output text. In the phonetic system, recognized speech is processed by using language processing i.e. This paper presents detailed study and performance evaluation of phonetic system by comparing it with various classification techniques of automatic speech recognition such as Neural Network, Hidden Markov Model, Support Vector Machine and Gaussian Mixture Model. The Hindi WordNet database provided by IIT Mumbai used in this research work for Hindi speech to text conversion. Proposed Phonetic Model supports multi-lingual speech recognition and observed that the accuracy of this system is 90% for Hindi and English speech to text recognition. This system combines the speech recognition approach used by these softwares and joined with language processing to improve the overall accuracy of the process with the help of phonetic analysis. Some existing speech recognition software's of Google, Amazon, and Microsoft tend to have an accuracy of more than 90% in real time speech detection. Here's the plan to improve the accuracy of this process with the help of natural language processing and speech analysis. Optimization of the speech recognition process is of utmost importance, due to the fact that real-time users want to perform actions based on the input speech given by them, and these actions sometime define the lifestyle of the users and thus the process of speech to text conversion should be carried out accurately. Speech recognition or speech to text conversion has rapidly gained a lot of interest by large organizations in order to ease the process of human to machine communication.